摘要
传统的mean-shift跟踪算法不能跟踪目标的旋转、缩放运动,且常常因此造成定位不准.鉴于此,将尺度不变特征变换(SIFT)特征检测融入到mean-shift跟踪过程,提出SIFT特征点的尺度变化与目标的尺度变化成正比,特征点主方向变化与目标旋转角度一致,给出了基于SIFT特征的自适应目标尺度、方向计算方法,且利用带方向、可变带宽的椭圆核改进传统的mean-shift跟踪方法.实验表明,该算法能够较好地跟踪目标的旋转、缩放运动,定位也更准确.
Traditional method can＇t follow the scale and orientation change of the object, which usually causes inaccurate localization. Therefore, this paper combines SIFt（scale invariant feature transform） feature detecting with the mean-shift tracking method, which proposes that the scale change of the SIFT keypoint is proportional to that of the object, and the dominant direction change of the SIFT keypoint is the same as that of the object. The algorithm for adaptively constructing the scale and the orientation information from SIFT features of the object is presented. And a variable-bandwidth and orientation ellipse kernel is used to improve the traditional mean-shift method. The experimental results show that the proposed algorithm provides good tracking of the scale and orientation change of the object, and the localization is more accurate.
出处
《控制与决策》
EI
CSCD
北大核心
2012年第3期399-402,407,共5页
Control and Decision
基金
江苏省科技支撑计划项目(BE2009667)
江苏省自然科学基金项目(BK2010366)
中兴通讯高校合作基金项目
